Algorithmic Models.
Neural Networks.
Applied AI Engineering.

# Specimen 1: PyTorch Convolutional Neural Network
import torch
import torch.nn as nn

class VisionClassifier(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2)
        )
        self.classifier = nn.Linear(64 * 8 * 8, num_classes)

    def forward(self, x):
        x = self.features(x)
        return self.classifier(torch.flatten(x, 1))

// Specimen 2: Hyperparameters & Training Telemetry
{
  "architecture": "CNN-Vision-v2",
  "optimizer": "AdamW",
  "learning_rate": 0.001,
  "batch_size": 64,
  "epochs": 12,
  "training_loss": [
    { "epoch": 1, "loss": 0.842, "val_acc": 0.712 },
    { "epoch": 6, "loss": 0.218, "val_acc": 0.914 },
    { "epoch": 12, "loss": 0.043, "val_acc": 0.968 }
  ]
}

# Specimen 3: Sentiment Vectorization & Classification
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

corpus = [
    "The autonomous drone detected obstacle immediately",
    "Sensor telemetry failed due to motor battery drain"
]

vectorizer = TfidfVectorizer(ngram_range=(1, 2))
X = vectorizer.fit_transform(corpus)
model = LogisticRegression()
model.fit(X, [1, 0]) # 1: Operational, 0: Alert